{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T13:49:23Z","timestamp":1657201763615},"reference-count":13,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2016,11]]},"abstract":"<jats:p>Boosting, one of the best off-the-shelf classification methods, has evoked widespread interest in machine learning and statistics. However, the original algorithm was developed for binary classification problems. In this paper, we study multi-class boosting algorithms under the [Formula: see text]-loss framework, and devise two multi-class [Formula: see text]-Boost algorithms. These are based on coordinate descent and gradient descent to minimize the multi-class [Formula: see text]-loss function. We derive a scoring coding scheme using optimal scoring constraints to encode class labels and a simple decoder to recover the true class labels. Our boosting algorithms are easily implemented and their results converge to the global optimum. Experiments with synthetic and real-world datasets show that, compared with several state-of-art methods, our algorithms provide more accurate results.<\/jats:p>","DOI":"10.1142\/s0219691316500491","type":"journal-article","created":{"date-parts":[[2016,8,31]],"date-time":"2016-08-31T06:55:35Z","timestamp":1472626535000},"page":"1650049","source":"Crossref","is-referenced-by-count":0,"title":["Multi-class \u21132-Boost with the scoring coding"],"prefix":"10.1142","volume":"14","author":[{"given":"Lei","family":"Ye","sequence":"first","affiliation":[{"name":"College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha 410073, P. R. 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